Waist-height ratio: How well does it predict glucose intolerance and systemic hypertension?

Obesity- and lipid-related indices as a predictor of type 2 diabetes in a national cohort study

Objective

Type 2 diabetes mellitus (T2DM) remains a major and widespread public health concern throughout the world. The prevalence of T2DM in the elderly has risen to the top of the list of public health concerns. In this study, obesity- and lipid-related indices were used to predict T2DM in middle-aged and elderly Chinese adults.

Methods

The data came from the China Health and Retirement Longitudinal Study (CHARLS), including 7902 middle-aged and elderly participants aged 45 years or above. The study assessed the association of obesity- and lipid-related indices and T2DM by measuring 13 indicators, including body mass index (BMI), waist circumference(WC), waist-height ratio (WHtR), conicity index(CI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-WC, TyG-WHtR). The association of 13 obesity- and lipid-related indices with T2DM was investigated by binary logistic regression. Additionally, the predictive anthropometric index was evaluated, and the ideal cut-off value was established using the receiver operating characteristic (ROC) curve analysis and area under the curve (AUC).

Results

The study included 7902 participants, of whom 3638(46.04) and 4264(53.96) were male and female. The prevalence of T2DM in mid-aged and old adults in China was 9.02% in males and 9.15% in females. All the above 13 indicators show a modest predictive power (AUC>0.5), which was significant for predicting T2DM in adults (middle-aged and elderly people) in China (P<0.05). The results revealed that TyG-WHtR [AUC =0.600, 95%CI: 0.566-0.634] in males and in females [AUC =0.664, 95%CI: 0.636-0.691] was the best predictor of T2DM (P<0.05).

Conclusion

Most obesity- and lipid-related indices have important value in predicting T2DM. Our results can provide measures for the early identification of T2DM in mid-aged and elderly Chinese to reduce the prevalence of T2DM and improve health.

Comparison of waist-height ratio and other obesity indices in the prediction of diabetic peripheral neuropathy

Background

Waist-height ratio (WHtR) is increasingly being studied as a simple and effective measure of central obesity. Reports have shown that WHtR is a better predictor of hypertension, diabetes, and cardiovascular diseases when compared to traditional obesity indices like body mass index (BMI), waist circumference (WC), and waist-hip ratio (WHR). This study is therefore aimed at comparing WHtR with other obesity indices in the prediction of peripheral neuropathy in persons with diabetes mellitus (DM).

Methodology

One thousand and forty persons with DM were enrolled following consent. Relevant details of history were obtained, followed by physical examinations. Data were analyzed using IBM-SPSS version 23. Logistic regression was used to compare the odds ratio of obesity indices in the prediction of peripheral neuropathy. The level of significance used was p = 0.05.

Results

Logistic regression showed that WHtR had the highest odds ratio (OR) for the prediction of "probable" diabetic peripheral neuropathy (OR 9.11, 95% CI 3.07-47.97, p = 0.002), followed by WC (OR 2.01, 95% CI 1.09-4.05, p = 0.004), and BMI (OR 1.26, 95% CI 1.00-3.99, p = 0.019) after correction for age; systemic hypertension; duration of DM; control of SBP, DBP, HbA1c, FPG, and 2HrPP.

Conclusion

WHtR has the highest odds ratio in the prediction of "probable" diabetic peripheral neuropathy in both genders, followed by WC in the males and BMI in the females.

Risk Prediction Model of Gestational Diabetes Mellitus in a Chinese Population Based on a Risk Scoring System

INTRODUCTION

Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system.

METHODS

This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively.

RESULTS

In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916).

CONCLUSION

The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.

Waist Circumference and its Changes Are More Strongly Associated with the Risk of Type 2 Diabetes than Body Mass Index and Changes in Body Weight in Chinese Adults

BACKGROUND

The associations of different adiposity indicators and short-term adiposity change with diabetes risk are not fully elucidated.

OBJECTIVE

We aimed to assess the independent and joint effects of different baseline adiposity indicators and short-term body adiposity change on the risk of type 2 diabetes.

METHODS

We prospectively followed 10,419 Chinese adults aged 20-80 y in 2008-2012. Incident diabetes was diagnosed based on fasting glucose, 2-h glucose, or glycated hemoglobin (HbA1c) after an oral glucose tolerance test using the American Diabetes Association standard. Cox proportional hazard regression models were used to assess the associations of adiposity indicators and adiposity change with diabetes risk.

RESULTS

During a mean follow-up of 2.8 y, we identified 805 type 2 diabetes cases. Baseline BMI, waist circumference, and waist-height ratio (WHtR) were all positively associated with diabetes risk. The area under the curve was significantly greater for waist circumference (0.624) and WHtR (0.627) than for BMI (0.608) (P <0.05). Compared with subjects with stable adiposity levels (±2 kg or ± 3 cm in changes in body weight or waist circumference) from baseline to Year 1, those subjects with the most weight gain or the most waist circumference gain had a 1.53-fold or 1.37-fold greater risk of diabetes; those with the most weight loss had a 46% lower risk of diabetes. Furthermore, regardless of baseline weight status, weight or waist circumference change in the first year was associated with diabetes risk.

CONCLUSION

Abdominal adiposity indicators, waist circumference and its change, are more strongly associated with the risk of type 2 diabetes than general adiposity indicators, BMI, and changes in body weight among Chinese adults.